Pith. sign in

REVIEW 6 major objections 4 minor 1 cited by

TRIPP: A General Purpose Data Pipeline for Astronomical Image Processing

T0 review · 6 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The TRIPP pipeline analyzes 6-megapixel astronomical images in under 3 seconds and matches published photometry of the supernova SN2023ixf.

desk verdict Useful pipeline paper, but the calibration equations are internally swapped and the SN2023ixf validation is not checkable as written. read the letter →

arxiv 2501.18142 v2 pith:NEKB6BPJ submitted 2025-01-30 astro-ph.IM

Alex Thomas , Natalie LeBaron , Luca Angeleri , Samuel Whitebook , Rachel Darlinger , Phillip Morgan , Varun Iyer , Prerana Kottapalli
show 21 more authors
Enda Mao Jasper Webb Dharv Patel Kyle Lam Kelvin Yip Michael McDonald Robby Odum Cole Slenkovich Yael Brynjegard-Bialik Nicole Efstathiu Joshua Perkins Ryan Kuo Audrey O'Malley Alec Wang Ben Fogiel Sam Salters Marlon Munoz Ruiyang Wang Natalie Kim Lee Fowler Philip Lubin
This is my paper · ORCID
classification astro-ph.IM
keywords transientdetectiondifferenceimaginglightcurveanalysisaperturephotometrySDSScalibrationsupernovaSN2023ixfopticalSETIreal-timeimageprocessing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents TRIPP, a modular difference-imaging and light-curve pipeline aimed at real-time transient detection. Its central claim is that a carefully optimized Bramich convolution-kernel subtraction, combined with SDSS-calibrated aperture photometry, lets a single workstation process 6-megapixel images in under three seconds. The authors validate the photometry by recovering the SN2023ixf light curve in agreement with two independent published analyses, and they show the pipeline can flag variable sources and, in principle, detect optical SETI signals. The broader point is that time-sensitive astronomy—supernovae, fast transients, technosignatures—does not have to wait for asynchronous batch processing.

What carries the argument

The load-bearing mechanism is the optimized Bramich kernel-subtraction routine: a convolution kernel is fit by least squares to match the PSF of each science image to the median template, the residual image is searched for sources, and a linear Orthogonal Distance Regression (ODR) between SDSS reference magnitudes and TRIPP instrumental magnitudes calibrates each frame. The ODR assumes equal error variance between the two catalogs (δ = 1), which yields the closed-form calibration coefficients. The speedup that makes the paper's real-time claim possible comes from GPU offloading and array-vectorized linear algebra applied to the Bramich kernel fit.

What would settle it

Compare TRIPP's calibrated magnitudes for a field of SDSS reference stars against a second independent catalog; a systematic residual trend with brightness would falsify the equal-variance ODR assumption.

Watch

Extended reading notes

Core claim

TRIPP's core discovery is that the standard Bramich image-subtraction algorithm, which typically takes about 30 seconds per image, can be optimized by offloading convolution to a GPU, vectorizing linear algebra with NumPy, and reusing kernel variables across frames, bringing subtraction time down to roughly 300 milliseconds. With this optimization the full pipeline—read, align, subtract, extract, write—runs in 2.19 ± 0.66 seconds per 6-megapixel frame. The paper further claims that the pipeline's light curves for SN2023ixf in the SDSS-r' band match the magnitudes reported by Hosseinzadeh et al. (2023) and Teja et al. (2023), and that its median-template-plus-SEP extraction can identify transient candidates in dense fields like M31, with a machine-learning YOLO-based extractor reaching a preliminary F1 of 0.72 compared to 0.38 for threshold-based SEP extraction.

Load-bearing premise

The calibration step treats the photometric errors of SDSS and TRIPP as equally uncertain (δ = 1); if the true error ratio is not unity, every calibrated magnitude—and therefore the SN2023ixf validation—shifts systematically.

Editorial extensions

If this is right

  • Difference imaging can be run at sub-second per-frame speeds on consumer GPU hardware, making real-time transient alerting feasible for small-aperture survey networks like LCOGT.
  • Because the pipeline is modular and open-source, the same alignment, template, subtraction, and calibration steps can be adapted to other telescopes and filters.
  • The photometric validation on SN2023ixf indicates that aperture photometry calibrated against SDSS is sufficient to recover a supernova light curve to a few tenths of a magnitude.
  • The YOLO-based extraction, once refined, could replace SEP and cut false positives by orders of magnitude, improving transient recall at the cost of some sensitivity.
  • TRIPP's detection of optical SETI transients in LGTS data suggests the same hardware and pipeline can be used to search for laser technosignatures toward M31 and the Magellanic Clouds.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the equal-variance assumption in the ODR calibration is relaxed, the same machinery could be applied to heterogeneous catalogs where quoted errors differ; the bias from 'unclean' SDSS stars could be quantified by re-running with the clean flag enforced.
  • The reported sub-3-second timing is hardware-specific; scaling the same optimization to the multi-gigapixel focal planes of next-generation synoptic surveys would likely require hierarchical or tiled processing, but the order-of-magnitude speedup suggests real-time sifting of difference images is within reach.
  • The F1 discrepancy between threshold-based extraction (0.38) and ML extraction (0.72) implies a precision-recall tradeoff; an operational pipeline might run both in parallel, using ML to triage alerts and SEP to recover missed faint candidates.
  • TRIPP's modular design means its calibration and variability-flagging steps could be reused for any time-domain survey, with the main barrier being the availability of overlapping SDSS reference stars.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 4 minor

Summary. The paper presents TRIPP, a Python pipeline for transient and variable source detection that combines difference imaging, aperture photometry, and light-curve analysis. It is tested on LGTS images from Las Cumbres Observatory 0.4 m telescopes and on SN2023ixf, with validation against published photometry. The paper also reports timing measurements for six-megapixel images, detection performance on simulated images using the F1 statistic, and preliminary results for a YOLO-based machine-learning extraction method.

Significance. If the reported performance holds, TRIPP would be a useful open-source tool for time-domain astronomy and optical SETI, with a demonstrated combination of sub-3-second processing (for the optimized Bramich path) and externally validated photometry. Strengths of the paper include validation against external data (published SN2023ixf photometry and simulated images with known transients), concrete timing measurements, a clear comparison of subtraction methods, and a stated plan to release the code under an MIT license. However, the photometric validation is currently uncheckable because of an internal inconsistency in the calibration equations and the absence of quantitative residuals, and the detection-performance summary is internally contradictory.

major comments (6)
  1. [§3.5, Eqs. (10)–(12)] The calibration equations are internally inconsistent. Equation (10) defines M = β0 mi + β1, making β0 the slope and β1 the intercept, while Eq. (11) gives β1 as the ODR slope and Eq. (12) gives β0 as the intercept. Since no code or data artifact is provided, a reader cannot determine which convention was actually used to produce Figure 10, and the central photometric validation is therefore uncheckable. Please fix the notation and make the implementation (or a minimal test case) available.
  2. [§4.3] The statement that the TRIPP magnitudes of SN2023ixf match those of Hosseinzadeh et al. (2023) and Teja et al. (2023) is not quantitatively supported. No residual statistics, per-epoch offsets, RMS, or comparison figures are given; the only quantitative statement is the 10 mmag systematic error in the Figure 10 caption, which is not derived anywhere in the text. Report the residuals between TRIPP and the published light curves (e.g., mean offset and scatter) and define how the 10 mmag systematic error was estimated.
  3. [§4.2, Table 2 and §6] There is an internal contradiction in the reported detection performance: the F1 score at 5 μ_bkg is 0.38 in Section 4.2 and Table 2, but 0.19 in the Conclusion. One of these values is wrong, and the discrepancy changes the characterization of the pipeline's transient detection capability. Correct the value and ensure the conclusion matches the results table.
  4. [§3.6, Eq. (17)] Equation (17), χ2_dof = χ2_dof, is a tautology; it should be χ2_dof = χ2/(n−1). This equation is load-bearing because the variability threshold χ2_dof > 25 and the assertion in Section 4.3 that a non-variable source has χ2_dof = 2.23 depend directly on the correct normalization of χ2_dof. Correct the equation and re-check the quoted χ2_dof values.
  5. [§3.5 and §3.4] The calibration assumes equal error variance between SDSS and TRIPP photometry (δ = 1) and uses an orthogonal distance regression, but no justification or sensitivity test for this assumption is provided. If the true variance ratio differs from 1, the calibrated magnitudes and hence the SN2023ixf comparison would be biased. In addition, the relaxation of the SDSS 'clean' photometry flag is adopted without quantifying how many reference stars are affected or what systematic offsets this choice can introduce. Provide a sensitivity analysis of the calibration to δ and to the 'clean' flag choice.
  6. [§6 and Table 1] The claim that 'TRIPP achieves reliable <3 second image analysis for 6 megapixel images' is not supported for all pipeline configurations presented in Table 1: only the NumPy and optimized Bramich paths meet the sub-3-second average, while SFFT averages 6 s and Adaptive Bramich averages 74 s. Moreover, the stated timings explicitly exclude light-curve creation, which is proportional to the number of transient candidates. Please state the claim as applying to the optimized Bramich default configuration and clearly define the end-to-end real-time analysis path.
minor comments (4)
  1. [§6] The conclusion contains the unresolved placeholder '(RECITE)' instead of the references to Hosseinzadeh et al. (2023) and Teja et al. (2023); these citations should be inserted.
  2. [Figure 10 caption] The statement that 'TRIPP's systematic error is 10 mmag' is not derived anywhere in the text; please provide the calculation or a reference to where it is defined.
  3. [Table 1] The Write row for NumPy lists '0.38 ± 0.16' without units, whereas other rows include 's'; this formatting inconsistency should be fixed.
  4. [§3.6, Eq. (13)] The weighted average magnitude Mavg is defined using instrumental magnitudes m_i, while Section 4.3 uses it to characterize variability of a calibrated source; please clarify whether the variability statistic uses instrumental or calibrated magnitudes and justify that choice.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: TRIPP's photometry and timing claims are validated against independent published photometry and simulated images, not against quantities fitted by the pipeline itself.

full rationale

The central derivation chain is self-contained. Section 3.5 calibrates TRIPP instrumental magnitudes using SDSS DR17 reference stars via an orthogonal distance regression, and the resulting coefficients are applied to SN2023ixf. The light curve is then compared in Section 4.3 with externally published photometry by Hosseinzadeh et al. (2023) and Teja et al. (2023). No parameter of the calibration is fitted to those published magnitudes, so the reported agreement is not forced by construction. The rapid-analysis claim in Table 1 is a direct benchmark measurement on the authors' hardware, and the F1 extraction results in Section 4.2 use simulated images with known injected transients, again independent of the pipeline's fitted outputs. The self-references to Thomas et al. (2025) and Lubin (2016) motivate the SETI use case but are not used as evidence for the photometric or timing claims, so they are not load-bearing in a circular sense. The inconsistency between Equation (10) and Equations (11)-(12) is a genuine correctness and reproducibility concern, as are the missing code artifact and the lack of quantitative residuals in the SN2023ixf comparison, but these are not circularity: they do not make the comparison reduce to its own input. The pipeline is validated against external benchmarks, so no circular step is exhibited.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The pipeline relies on several hand-chosen thresholds (extraction, variability, reference magnitude) and on calibration assumptions that are standard in photometry but not independently verified. No new physical entities are introduced.

free parameters (5)
  • Source extraction threshold = 5 µ_bkg (default)
    Set to 5 times the RMS background for LGTS data; configurable, chosen by hand.
  • Variability detection threshold = χ2_dof > 25
    Default threshold to flag variable sources; chosen to avoid flagging minor statistical variations.
  • Reference star magnitude threshold = magnitude 15 (typical)
    Used to select SDSS reference stars for calibration; user-defined.
  • Background annulus offsets = 5 pixels (inner and outer)
    Aperture photometry background annulus radii set to 5 pixels beyond the source aperture.
  • SDSS clean photometry flag = disabled (all stars allowed)
    The paper found the 'clean' flag too aggressive and allowed all SDSS stars as references, a post-hoc choice.
assumptions (5)
  • ad hoc to paper Equal error variance between SDSS and TRIPP photometry (δ = 1) in the ODR calibration.
    Assumed in Section 3.5 to express calibration coefficients directly; not empirically justified.
  • domain assumption SDSS DR17 magnitudes are accurate absolute references.
    Used to calibrate all TRIPP magnitudes; no external check on SDSS zero-points.
  • domain assumption BANZAI preprocessing provides correct WCS and calibrated images.
    The pipeline relies on BANZAI output for positions and flat-fielding.
  • domain assumption The median-combined template represents the static sky.
    Used for difference imaging; if the sky varies systematically, the template would introduce residuals.
  • standard math Standard error propagation formulas for aperture photometry.
    Equations (4) to (9) follow standard Poisson/readout noise assumptions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of TRIPP: A General Purpose Data Pipeline for Astronomical Image Processing." pith.science (2026). https://pith.science/paper/NEKB6BPJ

@misc{pith2026250118142,
  author       = {Pith},
  title        = {Pith review of: TRIPP: A General Purpose Data Pipeline for Astronomical Image Processing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NEKB6BPJ}},
  note         = {Machine review of arXiv:2501.18142}
}
read the original abstract

We present the TRansient Image Processing Pipeline (TRIPP), a transient and variable source detection pipeline that employs both difference imaging and light curve analysis techniques for astronomical data. Additionally, we demonstrate TRIPP's rapid analysis capability by detecting transient candidates in near-real time. TRIPP was tested using image data of the supernova SN2023ixf and from the Local Galactic Transient Survey (LGTS, Thomas et al. (2025)) collected by the Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. To verify the methods employed by TRIPP, we compare our results to published findings on the photometry of SN2023ixf. Additionally, we report the ability of TRIPP to detect transient signals from optical Search for Extra Terrestrial Intelligence (SETI) sources.

Figures

Figures reproduced from arXiv: 2501.18142 by the authors.

Figure 1
Figure 1. , we give an overview of our processing proce￾dures in the flowchart. We explain the data used to test TRIPP from our Lo￾cal Galactic Transient Survey (LGTS) in Section 2, the TRIPP pipeline in Section 3, and our results in Section 4. In Section 5, we discuss our ongoing efforts with and preliminary results utilizing a convolutional neural net￾work (You Only Look Once Darknet; Bochkovskiy et al. (2020)) for source e… view at source ↗
Figure 3
Figure 3. Andromeda LGTS survey sections. The sec￾tions of M31 imaged by LGTS prioritize galactic structure. Galactic image generated in Stellarium using a small FOV Mercator projection. 2.2. SN2023ixf Data We also observed the nearby supernova SN2023ixf to validate TRIPP’s photometry process and results. Our data for SN2023ixf spans May 25th through July 11th of 2023, again collected by the LCOGT Network’s ar￾ray of 0.4 m te… view at source ↗
Figure 2
Figure 2. Typical starfields for TRIPP with 100 second ex￾posure time for visibility. Images are BANZAI preprocessed. a) LGTS Section 38 taken at LCOGT’s McDonald Obser￾vatory on 2019 October 18 using an integration time of 60 seconds. b) LGTS Section 23 taken at LCOGT’s McDonald Observatory on 2019 July 19. c) LGTS Section 24 of M31 taken at LCOGT’s Haleakala Observatory on 2019 August 18. Since the angular size of Andromeda… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Median stacked image of the twenty-five 15 sec￾ond exposures from our first observing night. This 29.2′ x 19.5′ field of view contains SN2023ixf, a portion of M101, and SDSS cataloged reference stars. 3. TRANSIENT IMAGE PROCESSING PIPELINE (TRIPP) 3.1. Image Alignment …
Figure 5
Figure 5. Figure 5: Single 10 second exposure LGTS image of M31 section 41 that has only gone through preprocessing (a) ver￾sus the same image after the align step (b). The lines along the edges (b) are an artifact of the alignment process which the extract step excludes due to their size…
Figure 6
Figure 6. Figure 6: 10 second exposure LGTS images of M31 section 41 which showcase TRIPP’s image subtraction in a dense star field. Image A is a single image of the section from which the combined template Image B is subtracted via SFFT to achieve the residual Image C. In this image, the…
Figure 7
Figure 7. Figure 7: 10 second exposure LGTS images of M31 section 24 which showcase TRIPP’s image subtraction in a dense star field. In this image, the background average was 43.5 ADU with 64 sources above the 5µbkg threshold. observed sources. In order to extract sources, SEP first estim…
Figure 8
Figure 8. Figure 8: The locations of GPM 211.065031 +54.008123 and SN2023ixf identified in this field. The χ 2 dof for GPM 211.065031+54.008123 9 compared to a non-variable hypothesis using the weighted average magnitude is 2.23. Our calculated magnitudes are typ￾ically constrained to a f…
Figure 9
Figure 9. Figure 9: Light curve for GPM 211.065031+54.008123 spanning ten observing nights in SDSS-r’ band. Each ob￾servation consists of twenty-five 15 second exposures. The short integration time was selected to avoid saturation in SN2023ixf. Finally, we report a light curve of SN2023ix…
Figure 10
Figure 10. Figure 10: Light curve taken of SN2023ixf in SDSS-r’ band. Each observation consists of twenty-five 15 second exposures. TRIPP’s systematic error is 10 mmag. 5. FUTURE OF THE PROJECT Motivated by our desire to further reduce processing time and improve accuracy, we are testing m…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Local Galactic Transient Survey Applied to an Optical Search for Directed Intelligence

    astro-ph.IM 2025-01 conditional novelty 4.0 of 10

    A modest sky survey using existing small telescopes is calculated to be capable of detecting optical laser beacons from an advanced civilization in nearby galaxies.

Reference graph

Works this paper leans on

38 extracted references · 8 canonical work pages · cited by 1 Pith paper

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    2022, , 259, 35, 10.3847/1538-4365/ac4414

    Abdurro'uf , Accetta , K., Aerts , C., et al. 2022, , 259, 35, 10.3847/1538-4365/ac4414

    doi:10.3847/1538-4365/ac4414 2022

  4. [4]

    Alard, C., & Lupton, R. H. 1998, The Astrophysical Journal, 503, 325, 10.1086/305984

    doi:10.1086/305984 1998

  5. [5]

    2016, Journal of Open Source Software, 1, 58, 10.21105/joss.00058

    Barbary, K. 2016, Journal of Open Source Software, 1, 58, 10.21105/joss.00058

    doi:10.21105/joss.00058 2016

  6. [6]

    2014, in The Third Hot-wiring the Transient Universe Workshop, ed

    Bellm , E. 2014, in The Third Hot-wiring the Transient Universe Workshop, ed. P. R. Wozniak , M. J. Graham , A. A. Mahabal , & R. Seaman , 27--33. 1410.8185

    arXiv 2014

  7. [7]

    2020 a , Astronomy and Computing, 32, 100384, https://doi.org/10.1016/j.ascom.2020.100384

    Beroiz, M., Cabral, J., & Sanchez, B. 2020 a , Astronomy and Computing, 32, 100384, https://doi.org/10.1016/j.ascom.2020.100384

    arXiv 2020

  8. [8]

    2020 b , toros-astro/ois: Version 0.2, v0.2, Zenodo, 10.5281/zenodo.4042147

    Beroiz, M., Sánchez, B., & Iyer, V. 2020 b , toros-astro/ois: Version 0.2, v0.2, Zenodo, 10.5281/zenodo.4042147

    work page doi:10.5281/zenodo.4042147 2020

Show all 38 references
  1. [9]

    2009, , 80, 422

    Bertin, E. 2009, , 80, 422

    2009

  2. [10]

    1996, , 117, 393, 10.1051/aas:1996164

    Bertin , E., & Arnouts , S. 1996, , 117, 393, 10.1051/aas:1996164

    1996 doi

  3. [11]

    Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. 2020, YOLOv4: Optimal Speed and Accuracy of Object Detection, arXiv, 10.48550/ARXIV.2004.10934

    2020 doi arXiv

  4. [12]

    Z., Yang, M., Sokolovsky, K

    Bonanos, A. Z., Yang, M., Sokolovsky, K. V., et al. 2019, Astronomy and Astrophysics, 630, 10.1051/0004-6361/201936026

    2019 doi

  5. [13]

    Bramich, D. M. 2008, Monthly Notices of the Royal Astronomical Society: Letters, 386, 1, 10.1111/j.1745-3933.2008.00464.x

    2008

  6. [14]

    C., Magnier , E

    Chambers , K. C., Magnier , E. A., Metcalfe , N., et al. 2016, arXiv e-prints, arXiv:1612.05560. 1612.05560

    2016 arXiv

  7. [15]

    2009, in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248--255, 10.1109/CVPR.2009.5206848

    Deng, J., Dong, W., Socher, R., et al. 2009, in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248--255, 10.1109/CVPR.2009.5206848

    2009

  8. [16]

    J., Djorgovski, S

    Drake, A. J., Djorgovski, S. G., Mahabal, A., et al. 2009, Astrophysical Journal, 696, 870, 10.1088/0004-637X/696/1/870

    2009 doi

  9. [17]

    2001, The Mathematical Gazette, 85, 104, 10.2307/3620485

    Glaister, P. 2001, The Mathematical Gazette, 85, 104, 10.2307/3620485

    2001 doi

  10. [18]

    2023, in American Astronomical Society Meeting Abstracts, Vol

    Harbeck , D., Taylor , B., Kirby , A., et al. 2023, in American Astronomical Society Meeting Abstracts, Vol. 55, American Astronomical Society Meeting Abstracts, 205.04

    2023

  11. [19]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, 10.1038/s41586-020-2649-2

    2020 doi

  12. [20]

    2018, Journal of Astrophysics and Astronomy, 39, 73, 10.1007/s12036-018-9566-x

    Hippke , M. 2018, Journal of Astrophysics and Astronomy, 39, 73, 10.1007/s12036-018-9566-x

    2018 doi

  13. [21]

    2023, The Astrophysical Journal Letters, 953, L16, 10.3847/2041-8213/ace4c4

    Hosseinzadeh, G., Farah, J., Shrestha, M., et al. 2023, The Astrophysical Journal Letters, 953, L16, 10.3847/2041-8213/ace4c4

    2023 doi

  14. [22]

    2022, The Astrophysical Journal, 936, 157, 10.3847/1538-4357/ac7394

    Hu, L., Wang, L., Chen, X., & Yang, J. 2022, The Astrophysical Journal, 936, 157, 10.3847/1538-4357/ac7394

    2022 doi

  15. [23]

    E., Adams, S

    Jencson, J. E., Adams, S. M., Bond, H. E., et al. 2019, The Astrophysical Journal, 880, L20, 10.3847/2041-8213/ab2c05

    2019 doi

  16. [24]

    B., Dobler, G., et al

    Li, X., Bianco, F. B., Dobler, G., et al. 2022, Toward automated detection of light echoes in synoptic surveys: considerations on the application of the Deep Convolutional Neural Networks. 2208.04198

    2022 arXiv

  17. [25]

    A., Allison, J., et al

    LSST Science Collaboration , Abell, P. A., Allison, J., et al. 2009. 0912.0201

    2009 arXiv

  18. [26]

    2016, Reviews in Human Space Exploration, 58, 10.1016/j.reach.2016.05.003

    Lubin, P. 2016, Reviews in Human Space Exploration, 58, 10.1016/j.reach.2016.05.003

    2016 doi

  19. [27]

    P., Pennypacker , C

    Miller , J. P., Pennypacker , C. R., & White , G. L. 2008, , 120, 449, 10.1086/588258

    2008 doi

  20. [28]

    I., Hatzidimitriou, D., Karampelas, A., et al

    Moretti, M. I., Hatzidimitriou, D., Karampelas, A., et al. 2018, 10.1093/mnras/sty758

    2018 doi

  21. [29]

    A., Menanteau, F., et al

    Morganson, E., Gruendl, R. A., Menanteau, F., et al. 2018, Publications of the Astronomical Society of the Pacific, 130, 10.1088/1538-3873/aab4ef

    2018 doi

  22. [30]

    2002, , 52, 397

    Pojmanski , G. 2002, , 52, 397. astro-ph/0210283

    2002 arXiv

  23. [31]

    C., Enriquez , J

    Price , D. C., Enriquez , J. E., Brzycki , B., et al. 2020, , 159, 86, 10.3847/1538-3881/ab65f1

    2020 doi

  24. [32]

    2013--2016, Darknet: Open Source Neural Networks in C, http://pjreddie.com/darknet/

    Redmon, J. 2013--2016, Darknet: Open Source Neural Networks in C, http://pjreddie.com/darknet/

    2013

  25. [33]

    W., Tanaka, M., Morokuma, T., et al

    Richmond, M. W., Tanaka, M., Morokuma, T., et al. 2019, 10.1093/pasj/psz120

    2019 doi

  26. [34]

    2001, Annual Review of Astronomy and Astrophysics, 39, 511, 10.1146/annurev.astro.39.1.511

    Tarter, J. 2001, Annual Review of Astronomy and Astrophysics, 39, 511, 10.1146/annurev.astro.39.1.511

    2001 doi

  27. [35]

    S., Singh, A., Basu, J., et al

    Teja, R. S., Singh, A., Basu, J., et al. 2023, The Astrophysical Journal Letters, 954, L12, 10.3847/2041-8213/acef20

    2023 doi

  28. [36]

    2025, The Local Galactic Transient Survey Applied to an Optical Search for Directed Intelligence

    Thomas, A., LeBaron, N., Angeleri, L., et al. 2025, The Local Galactic Transient Survey Applied to an Optical Search for Directed Intelligence. 2501.18903

    2025 arXiv

  29. [37]

    L., Denneau , L., Heinze , A

    Tonry , J. L., Denneau , L., Heinze , A. N., et al. 2018, , 130, 064505, 10.1088/1538-3873/aabadf

    2018 doi

  30. [38]

    O., & Gal-Yam, A

    Zackay, B., Ofek, E. O., & Gal-Yam, A. 2016, The Astrophysical Journal, 830, 27, 10.3847/0004-637x/830/1/27

    2016 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.